1. Field of Invention
The present disclosure is related to biometric authentication, and more specifically, to a relational database management system that may perform biometric authentication.
2. Description of Related Art
The need to establish personal identity occurs, for most individuals, many times a day. For example, a person may have to establish identity in order to gain access to physical spaces, computers, bank accounts, personal records, restricted areas, reservations, and the like. Identity is typically established by something we have (e.g., a key, driver license, bank card, credit card, etc.), something we know (e.g., computer password, PIN number, etc.), or some unique and measurable biological feature (e.g., our face recognized by a bank teller or security guard, etc.). The most secure means of identity is a biological (or behavioral) feature that can be objectively and automatically measured and is resistant to impersonation, theft, or other fraud. The use of biometrics, which are measurements derived from human biological features, to identify individuals is a rapidly emerging science.
Biometrics is a generic term for characteristics that can be used to distinguish one individual from another, particularly through the use of digital equipment. An example of a biometric is a fingerprint. Trained analysts have long been able to match fingerprints in order to identify individuals. More recently, computer systems have been developed to match fingerprints automatically. Examples of biometrics used to identify or authenticate the identity of individuals include 2D face, 3D face, hand geometry, single fingerprint, ten finger live scan, iris, palm, full hand, signature, ear, finger vein, retina, DNA and voice. Other biometric may include characteristic gaits, lip movements and the like. New biometric are being developed or discovered continually.
It is common to employ a relational database management systems (RDBMS) to manage biometric data. Such relational database management systems are not designed specifically for the processing of biometric data and may often impose resource-intensive processes, thereby utilizing a high number of computing resources (e.g., power, processor storage, memory, and/or network capacity). The resource utilization may incur a significant delay in performing the biometric analysis and matching requests.
In justice and law enforcement, biometrics technology may be applicable for analyzing crime scenes and suspects. Biometric databases for law enforcement applications may contain thousands or millions of records and the identification of a criminal or suspect may need to be determined in a fast and accurate way before they may cause harm to someone. Thus, there exists the need for an effective database architecture model for managing biometric data in a RDBMS.
U.S. Pat. No. 7,689,005 issued to Wang et al. discloses a method and system for constructing a database management system for managing biometric data. The disclosed system in Wang receives data from another database or from and enrollment process, encodes the data with an encoding plug-in, and stores the encoded data in a biometric data storage. The data may be enhanced before being stored. Incoming target data likewise is encoded using an encoding plug-in and may be pre-processed, and is sent to a matching algorithm that is either built-in or a plug-in algorithm.
U.S. Pat. No. 7,949,156 issued to Willis et al. discloses a method for analyzing a dataset comprising biographic data and biometric data. In one step, a biographic record is read that is normally meant for unique description of an individual. A biometric associated with the biographic record is also read. The biometric is correlated with a plurality of biometrics associated with other biographic records. The uniqueness of the biometric is assessed with respect to the plurality of biometrics, for example, to find duplicate biographic records with biometric matching.
However, the prior art falls short because it does not convert biometric data into a biometric template that can be compared to a plurality of stored biometric templates to see if there is a match and does not process biometric data separately from demographic data allowing a faster and more accurate execution of queries including both demographic and biometric constraints.